Timeseries: Add "grandTotal" option. (#5640)

* Timeseries: Add "grandTotal" option.

* Modify whitespace.

* Checkstyle workaround.

docs/content/querying/timeseriesquery.md

@@ -56,7 +56,7 @@ There are 7 main parts to a timeseries query:
 |filter|See [Filters](../querying/filters.html)|no|
 |aggregations|See [Aggregations](../querying/aggregations.html)|no|
 |postAggregations|See [Post Aggregations](../querying/post-aggregations.html)|no|
-|context|See [Context](../querying/query-context.html)|no|
+|context|Can be used to modify query behavior, including [grand totals](#grand-totals) and [zero-filling](#zero-filling). See also [Context](../querying/query-context.html) for parameters that apply to all query types.|no|
 
 To pull it all together, the above query would return 2 data points, one for each day between 2012-01-01 and 2012-01-03, from the "sample\_datasource" table. Each data point would be the (long) sum of sample\_fieldName1, the (double) sum of sample\_fieldName2 and the (double) result of sample\_fieldName1 divided by sample\_fieldName2 for the filter set. The output looks like this:
 
@@ -73,6 +73,31 @@ To pull it all together, the above query would return 2 data points, one for eac
 ]
 ```
 
+#### Grand totals
+
+Druid can include an extra "grand totals" row as the last row of a timeseries result set. To enable this, add
+`"grandTotal" : true` to your query context. For example:
+
+```json
+{
+  "queryType": "timeseries",
+  "dataSource": "sample_datasource",
+  "intervals": [ "2012-01-01T00:00:00.000/2012-01-03T00:00:00.000" ],
+  "granularity": "day",
+  "aggregations": [
+    { "type": "longSum", "name": "sample_name1", "fieldName": "sample_fieldName1" },
+    { "type": "doubleSum", "name": "sample_name2", "fieldName": "sample_fieldName2" }
+  ],
+  "context": {
+    "grandTotal": true
+  }
+}
+```
+
+The grand totals row will appear as the last row in the result array, and will have no timestamp. It will be the last
+row even if the query is run in "descending" mode. Post-aggregations in the grand totals row will be computed based
+upon the grand total aggregations.
+
 #### Zero-filling
 
 Timeseries queries normally fill empty interior time buckets with zeroes. For example, if you issue a "day" granularity

processing/src/main/java/io/druid/query/timeseries/TimeseriesQuery.java

@@ -45,6 +45,8 @@ import java.util.Objects;
 @JsonTypeName("timeseries")
 public class TimeseriesQuery extends BaseQuery<Result<TimeseriesResultValue>>
 {
+  static final String CTX_GRAND_TOTAL = "grandTotal";
+
   private final VirtualColumns virtualColumns;
   private final DimFilter dimFilter;
   private final List<AggregatorFactory> aggregatorSpecs;
@@ -117,6 +119,11 @@ public class TimeseriesQuery extends BaseQuery<Result<TimeseriesResultValue>>
     return postAggregatorSpecs;
   }
 
+  public boolean isGrandTotal()
+  {
+    return getContextBoolean(CTX_GRAND_TOTAL, false);
+  }
+
   public boolean isSkipEmptyBuckets()
   {
     return getContextBoolean("skipEmptyBuckets", false);

processing/src/main/java/io/druid/query/timeseries/TimeseriesQueryQueryToolChest.java

@@ -23,12 +23,14 @@ import com.fasterxml.jackson.core.type.TypeReference;
 import com.google.common.annotations.VisibleForTesting;
 import com.google.common.base.Function;
 import com.google.common.collect.ImmutableList;
+import com.google.common.collect.ImmutableMap;
 import com.google.common.collect.Lists;
 import com.google.common.collect.Maps;
 import com.google.common.collect.Ordering;
 import com.google.inject.Inject;
 import io.druid.java.util.common.granularity.Granularity;
 import io.druid.java.util.common.guava.Sequence;
+import io.druid.java.util.common.guava.Sequences;
 import io.druid.java.util.common.guava.nary.BinaryFn;
 import io.druid.query.CacheStrategy;
 import io.druid.query.IntervalChunkingQueryRunnerDecorator;
@@ -46,6 +48,8 @@ import io.druid.query.cache.CacheKeyBuilder;
 import org.joda.time.DateTime;
 
 import javax.annotation.Nullable;
+import java.util.Collections;
+import java.util.HashMap;
 import java.util.Iterator;
 import java.util.List;
 import java.util.Map;
@@ -88,7 +92,8 @@ public class TimeseriesQueryQueryToolChest extends QueryToolChest<Result<Timeser
       QueryRunner<Result<TimeseriesResultValue>> queryRunner
   )
   {
-    return new ResultMergeQueryRunner<Result<TimeseriesResultValue>>(queryRunner)
+    final QueryRunner<Result<TimeseriesResultValue>> resultMergeQueryRunner = new ResultMergeQueryRunner<Result<TimeseriesResultValue>>(
+        queryRunner)
     {
       @Override
       public Sequence<Result<TimeseriesResultValue>> doRun(
@@ -125,6 +130,71 @@ public class TimeseriesQueryQueryToolChest extends QueryToolChest<Result<Timeser
         );
       }
     };
+
+    return new QueryRunner<Result<TimeseriesResultValue>>()
+    {
+      @Override
+      public Sequence<Result<TimeseriesResultValue>> run(
+          final QueryPlus<Result<TimeseriesResultValue>> queryPlus,
+          final Map<String, Object> responseContext
+      )
+      {
+        final TimeseriesQuery query = (TimeseriesQuery) queryPlus.getQuery();
+        final Sequence<Result<TimeseriesResultValue>> baseResults = resultMergeQueryRunner.run(
+            queryPlus.withQuery(
+                queryPlus.getQuery()
+                         .withOverriddenContext(
+                             ImmutableMap.of(TimeseriesQuery.CTX_GRAND_TOTAL, false)
+                         )
+            ),
+            responseContext
+        );
+
+        if (query.isGrandTotal()) {
+          // Accumulate grand totals while iterating the sequence.
+          final Object[] grandTotals = new Object[query.getAggregatorSpecs().size()];
+          final Sequence<Result<TimeseriesResultValue>> mappedSequence = Sequences.map(
+              baseResults,
+              resultValue -> {
+                for (int i = 0; i < query.getAggregatorSpecs().size(); i++) {
+                  final AggregatorFactory aggregatorFactory = query.getAggregatorSpecs().get(i);
+                  final Object value = resultValue.getValue().getMetric(aggregatorFactory.getName());
+                  if (grandTotals[i] == null) {
+                    grandTotals[i] = value;
+                  } else {
+                    grandTotals[i] = aggregatorFactory.combine(grandTotals[i], value);
+                  }
+                }
+                return resultValue;
+              }
+          );
+
+          return Sequences.concat(
+              ImmutableList.of(
+                  mappedSequence,
+                  Sequences.simple(
+                      () -> {
+                        final Map<String, Object> totalsMap = new HashMap<>();
+
+                        for (int i = 0; i < query.getAggregatorSpecs().size(); i++) {
+                          totalsMap.put(query.getAggregatorSpecs().get(i).getName(), grandTotals[i]);
+                        }
+
+                        final Result<TimeseriesResultValue> result = new Result<>(
+                            null,
+                            new TimeseriesResultValue(totalsMap)
+                        );
+
+                        return Collections.singletonList(result).iterator();
+                      }
+                  )
+              )
+          );
+        } else {
+          return baseResults;
+        }
+      }
+    };
   }
 
   @Override

processing/src/test/java/io/druid/query/timeseries/TimeseriesQueryRunnerTest.java

@@ -70,6 +70,7 @@ import org.junit.Test;
 import org.junit.runner.RunWith;
 import org.junit.runners.Parameterized;
 
+import java.util.ArrayList;
 import java.util.Arrays;
 import java.util.Collections;
 import java.util.HashMap;
@@ -389,6 +390,101 @@ public class TimeseriesQueryRunnerTest
     assertExpectedResults(expectedResults, results);
   }
 
+  @Test
+  public void testTimeseriesGrandTotal()
+  {
+    TimeseriesQuery query = Druids.newTimeseriesQueryBuilder()
+                                  .dataSource(QueryRunnerTestHelper.dataSource)
+                                  .granularity(QueryRunnerTestHelper.dayGran)
+                                  .intervals(QueryRunnerTestHelper.firstToThird)
+                                  .aggregators(
+                                      Arrays.asList(
+                                          QueryRunnerTestHelper.rowsCount,
+                                          QueryRunnerTestHelper.indexLongSum,
+                                          QueryRunnerTestHelper.qualityUniques
+                                      )
+                                  )
+                                  .postAggregators(QueryRunnerTestHelper.addRowsIndexConstant)
+                                  .descending(descending)
+                                  .context(ImmutableMap.of(TimeseriesQuery.CTX_GRAND_TOTAL, true))
+                                  .build();
+
+    List<Result<TimeseriesResultValue>> expectedResults = new ArrayList<>();
+
+    expectedResults.add(
+        new Result<>(
+            DateTimes.of("2011-04-01"),
+            new TimeseriesResultValue(
+                ImmutableMap.of(
+                    "rows",
+                    13L,
+                    "index",
+                    6619L,
+                    "uniques",
+                    QueryRunnerTestHelper.UNIQUES_9,
+                    QueryRunnerTestHelper.addRowsIndexConstantMetric,
+                    6633.0
+                )
+            )
+        )
+    );
+
+    expectedResults.add(
+        new Result<>(
+            DateTimes.of("2011-04-02"),
+            new TimeseriesResultValue(
+                ImmutableMap.of(
+                    "rows",
+                    13L,
+                    "index",
+                    5827L,
+                    "uniques",
+                    QueryRunnerTestHelper.UNIQUES_9,
+                    QueryRunnerTestHelper.addRowsIndexConstantMetric,
+                    5841.0
+                )
+            )
+        )
+    );
+
+    if (descending) {
+      Collections.reverse(expectedResults);
+    }
+
+    expectedResults.add(
+        new Result<>(
+            null,
+            new TimeseriesResultValue(
+                ImmutableMap.of(
+                    "rows",
+                    26L,
+                    "index",
+                    12446L,
+                    "uniques",
+                    QueryRunnerTestHelper.UNIQUES_9,
+                    QueryRunnerTestHelper.addRowsIndexConstantMetric,
+                    12473.0
+                )
+            )
+        )
+    );
+
+    // Must create a toolChest so we can run mergeResults (which applies grand totals).
+    QueryToolChest<Result<TimeseriesResultValue>, TimeseriesQuery> toolChest = new TimeseriesQueryQueryToolChest(
+        QueryRunnerTestHelper.NoopIntervalChunkingQueryRunnerDecorator()
+    );
+
+    // Must wrapped in a results finalizer to stop the runner's builtin finalizer from being called.
+    final FinalizeResultsQueryRunner finalRunner = new FinalizeResultsQueryRunner(
+        toolChest.mergeResults(runner),
+        toolChest
+    );
+
+    final List results = finalRunner.run(QueryPlus.wrap(query), CONTEXT).toList();
+
+    TestHelper.assertExpectedResults(expectedResults, results);
+  }
+
   @Test
   public void testTimeseriesWithVirtualColumn()
   {

processing/src/test/java/io/druid/segment/TestHelper.java

@@ -302,8 +302,8 @@ public class TestHelper
     // always generate exactly the same results (different merge ordering / float vs double)
     Assert.assertEquals(
         StringUtils.format("%s: timestamp", msg),
-        expected.getTimestamp().getMillis(),
+        expected.getTimestamp(),
-        actual.getTimestamp().getMillis()
+        actual.getTimestamp()
     );
 
     final Map<String, Object> expectedMap = ((MapBasedRow) expected).getEvent();